It facilitates the calculation of 40 different insulin sensitivity indices based on fasting, oral glucose tolerance test (OGTT), lipid (adipose), and tracer (palmitate and glycerol rate) and dxa (fat mass) measurement values. It enables easy and accurate assessment of insulin sensitivity, critical for understanding and managing metabolic disorders like diabetes and obesity. Indices calculated are described in Gastaldelli (2022). <doi:10.1002/oby.23503> and Lorenzo (2010). <doi:10.1210/jc.2010-1144>.

The 2017 American College of Cardiology and American Heart Association blood pressure guideline recommends using 10-year predicted atherosclerotic cardiovascular disease risk to guide the decision to initiate or intensify antihypertensive medication. The guideline recommends using the Pooled Cohort risk prediction equations to predict 10-year atherosclerotic cardiovascular disease risk. This package implements the original Pooled Cohort risk prediction equations and also incorporates updated versions based on more contemporary data and statistical methods.

This package provides R functions for calculating basic effect size indices for single-case designs, including several non-overlap measures and parametric effect size measures, and for estimating the gradual effects model developed by Swan and Pustejovsky (2018) <DOI:10.1080/00273171.2018.1466681>. Standard errors and confidence intervals (based on the assumption that the outcome measurements are mutually independent) are provided for the subset of effect sizes indices with known sampling distributions.

This package provides a time series causal inference model for Randomized Controlled Trial (RCT) under spillover effect. SPORTSCausal (Spillover Time Series Causal Inference) separates treatment effect and spillover effect from given responses of experiment group and control group by predicting the response without treatment. It reports both effects by fitting the Bayesian Structural Time Series (BSTS) model based on CausalImpact', as described in Brodersen et al. (2015) <doi:10.1214/14-AOAS788>.

This package provides a color picker that can be used as an input in Shiny apps or Rmarkdown documents. The color picker supports alpha opacity, custom color palettes, and many more options. A plot color helper tool is available as an RStudio Addin, which helps you pick colors to use in your plots. A more generic color picker RStudio Addin is also provided to let you select colors to use in your R code.

This Haskell package contains code for generating high quality random numbers that follow either a uniform or normal distribution. The generated numbers are suitable for use in statistical applications.

The uniform PRNG uses Marsaglia's MWC256 (also known as MWC8222) multiply-with-carry generator, which has a period of 2^8222 and fares well in tests of randomness. It is also extremely fast, between 2 and 3 times faster than the Mersenne Twister.

Propshaft is an asset pipeline library for Rails. It's built for an era where bundling assets to save on HTTP connections is no longer urgent, where JavaScript and CSS are either compiled by dedicated Node.js bundlers or served directly to the browsers, and where increases in bandwidth have made the need for minification less pressing. These factors allow for a dramatically simpler and faster asset pipeline compared to previous options, like Sprockets.

This package implements an MCMC algorithm to estimate a hierarchical multinomial logit model with a normal heterogeneity distribution. The algorithm uses a hybrid Gibbs Sampler with a random walk metropolis step for the MNL coefficients for each unit. Dependent variable may be discrete or continuous. Independent variables may be discrete or continuous with optional order constraints. Means of the distribution of heterogeneity can optionally be modeled as a linear function of unit characteristics variables.

This package provides extra functions to manipulate dendrograms that build on the base functions provided by the stats package. The main functionality it is designed to add is the ability to colour all the edges in an object of class dendrogram according to cluster membership i.e. each subtree is coloured, not just the terminal leaves. In addition it provides some utility functions to cut dendrogram and hclust objects and to set/get labels.

Easily load and install multiple packages from different sources, including CRAN and GitHub. The libraries function allows you to load or attach multiple packages in the same function call. The packages function will load one or more packages, and install any packages that are not installed on your system (after prompting you). Also included is a from_import function that allows you to import specific functions from a package into the global environment.

This package provides a function that wraps mcparallel() and mccollect() from parallel with temporary variables and a task handler. Wrapped in this way the results of an mcparallel() call can be returned to the R session when the fork is complete without explicitly issuing a specific mccollect() to retrieve the value. Outside of top-level tasks, multiple mcparallel() jobs can be retrieved with a single call to mcparallelDoCheck().

Extend the functionality of the mclust package for Gaussian finite mixture modeling by including: density estimation for data with bounded support (Scrucca, 2019 <doi:10.1002/bimj.201800174>); modal clustering using MEM (Modal EM) algorithm for Gaussian mixtures (Scrucca, 2021 <doi:10.1002/sam.11527>); entropy estimation via Gaussian mixture modeling (Robin & Scrucca, 2023 <doi:10.1016/j.csda.2022.107582>); Gaussian mixtures modeling of financial log-returns (Scrucca, 2024 <doi:10.3390/e26110907>).

This package provides methods to calculate and present PHENTHAUproc', an early warning and decision support system for hazard assessment and control of oak processionary moth (OPM) using local and spatial temperature data. It was created by Halbig et al. 2024 (<doi:10.1016/j.foreco.2023.121525>) at FVA (<https://www.fva-bw.de/en/homepage/>) Forest Research Institute Baden-Wuerttemberg, Germany and at BOKU - University of Natural Ressources and Life Sciences, Vienna, Austria.

This package provides a wrapper to access data from the SeeClickFix web API for R. SeeClickFix is a central platform employed by many cities that allows citizens to request their city's services. This package creates several functions to work with all the built-in calls to the SeeClickFix API. Allows users to download service request data from numerous locations in easy-to-use dataframe format manipulable in standard R functions.

This package provides tools for power and sample size calculation as well as design diagnostics for longitudinal mixed model settings, with a focus on stepped wedge designs. All calculations are oracle estimates i.e. assume random effect variances to be known (or guessed) in advance. The method is introduced in Hussey and Hughes (2007) <doi:10.1016/j.cct.2006.05.007>, extensions are discussed in Li et al. (2020) <doi:10.1177/0962280220932962>.

This package provides a collection of data sets relating to ADHD (Attention Deficit Hyperactivity Disorder) which have been sourced from other packages on CRAN or from publications on other websites such as Kaggle <http://www.kaggle.com/>.The package also includes some simple functions for analysing data sets. The data sets and descriptions of the data sets may differ from what is on CRAN or other source websites. The aim of this package is to bring together data sets from a variety of ADHD research publications. This package would be useful for those interested in finding out what research has been done on the topic of ADHD, or those interested in comparing the results from different existing works. I started this project because I wanted to put together a collection of the data sets relevant to ADHD research, which I have a personal interest in. This work was conducted with the support of my mentor within the Global Talent Mentoring platform. <https://globaltalentmentoring.org/>.

This package provides functions for blind source separation over multivariate spatial data, and useful statistics for evaluating performance of estimation on mixing matrix. BSSoverSpace is based on an eigen analysis of a positive definite matrix defined in terms of multiple normalized spatial local covariance matrices, and thus can handle moderately high-dimensional random fields. This package is an implementation of the method described in Zhang, Hao and Yao (2022)<arXiv:2201.02023>.

Generates robust confidence intervals for standardized regression coefficients using heteroskedasticity-consistent standard errors for models fitted by lm() as described in Dudgeon (2017) <doi:10.1007/s11336-017-9563-z>. The package can also be used to generate confidence intervals for R-squared, adjusted R-squared, and differences of standardized regression coefficients. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.

Maps one of the viridis colour palettes, or a user-specified palette to values. Viridis colour maps are created by Stéfan van der Walt and Nathaniel Smith, and were set as the default palette for the Python Matplotlib library <https://matplotlib.org/>. Other palettes available in this library have been derived from RColorBrewer <https://CRAN.R-project.org/package=RColorBrewer> and colorspace <https://CRAN.R-project.org/package=colorspace> packages.

Calculates confidence intervals after variable selection using repeated data splits. The package offers methods to address the challenges of post-selection inference, ensuring more accurate confidence intervals in models involving variable selection. The two main functions are lmps', which records the different models selected across multiple data splits as well as the corresponding coefficient estimates, and cips', which takes the lmps object as input to select variables and perform inferences using two types of voting.

Multiple comparison procedures (MCPs) control the familywise error rate in clinical trials. Graphical MCPs include many commonly used procedures as special cases; see Bretz et al. (2011) <doi:10.1002/bimj.201000239>, Lu (2016) <doi:10.1002/sim.6985>, and Xi et al. (2017) <doi:10.1002/bimj.201600233>. This package is a low-dependency implementation of graphical MCPs which allow mixed types of tests. It also includes power simulations and visualization of graphical MCPs.

This package provides functions for determining and evaluating high-risk zones and simulating and thinning point process data, as described in Determining high risk zones using point process methodology - Realization by building an R package Seibold (2012) <http://highriskzone.r-forge.r-project.org/Bachelorarbeit.pdf> and Determining high-risk zones for unexploded World War II bombs by using point process methodology', Mahling et al. (2013) <doi:10.1111/j.1467-9876.2012.01055.x>.

This package provides functions to compute Euclidean minimum spanning trees using single-, sesqui-, and dual-tree Boruvka algorithms. Thanks to K-d trees, they are fast in spaces of low intrinsic dimensionality. Mutual reachability distances (used in the definition of the HDBSCAN* algorithm) are also supported. The package also features relatively fast fallback minimum spanning tree and nearest-neighbours algorithms for spaces of higher dimensionality. The Python version of quitefastmst is available via PyPI'.

This package provides a macro package for use with epsf.tex which allows PostScript labels in an Encapsulated PostScript file to be replaced by TeX labels. The package provides commands \relabel (simply replace a PostScript string), \adjustrelabel (replace a PostScript string, with position adjustment), and \extralabel (add a label at given coordinates). You can, if you so choose, use the facilities of the labelfig package in place of using \extralabel.